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Record W4289518455 · doi:10.1021/acsestwater.2c00167

Drivers of Declining Water Access in Alaska

2022· article· en· W4289518455 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueACS ES&T Water · 2022
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsQuarter (Canadian coin)RentingCensusGovernment (linguistics)BusinessSocial securityAmerican Community SurveyService (business)GeographyDemographic economicsCapital (architecture)Economic growthEconomicsSocioeconomicsDemographyEngineeringMarketing

Abstract

fetched live from OpenAlex

A majority of homes in the United States (US) receive household water services via complete in-home plumbing. Observers tend to assume that in the US, there is an upward trend in plumbing access; yet in some Alaska communities, the rate is in fact a downward trend. This study seeks to identify, while considering the spatiotemporal variations in the region, the sociodemographic parameters that are correlated with the rates of in-home plumbing in Alaska communities. Equipped with American Community Survey data from 2011 to 2015, we employed a fixed-effects regression analysis. Our findings show that, concerning complete in-home plumbing, there was a statistically significant decrease in close to a quarter (23%) of census-designated places in Alaska. Access to complete plumbing is correlated to multiple sociodemographic characteristics, including the percentage of households that (1) receive social security, (2) are valued under $150,000, and (3) are renter-occupied units paying for one or more utilities. Our results help decision-makers efficiently allocate government funds by showing where service is deteriorating as well as the potential predictors of such decline. Our study reveals the pressing need to invest in not only new water systems but also maintenance, operations, and capital improvements.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.291
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it